Lean Six Sigma Logic Reshapes How Doctoral Researchers Frame a Problem

A new white paper from the University of Phoenix College of Doctoral Studies proposes borrowing the structured gap logic of Lean Six Sigma to help doctoral researchers build one of the most consequential documents in a dissertation: the problem statement. The paper, titled “Building a Lean Six Sigma–Based Background of the Problem and Problem Statement,” was written by James C. Rice, DM/IST, MBA, and Scott A. McCalla, DM, MA, and published on 12 October 2026. Rather than reporting empirical findings, the authors present a conceptual and drafting framework intended to guide practitioner researchers in business as they move from an observed workplace condition to a defensible, evidence-based research problem.

The framework adapts the Define and Measure phases of the Lean Six Sigma DMAIC cycle, which stands for Define, Measure, Analyze, Improve and Control. In quality-improvement practice, DMAIC is commonly used to examine performance and improvement, and its early phases require practitioners to specify exactly what process is under examination, which population and setting it involves, and what measurable evidence documents the gap between current and desired performance. Rice and McCalla argue that the same discipline can be transferred to doctoral research design. In their application, Define directs the researcher to specify the population, setting, process, indicator, stakeholders and boundaries of the problem, while Measure requires documenting the observed condition and an authoritative normative condition using comparable evidence.

“Defining the problem is one of the most consequential steps in practitioner research because every decision that follows depends on that foundation,” Rice said in the university’s announcement. “Our framework gives researchers a disciplined way to move from an observed condition to a researchable problem using evidence that can be examined, compared and verified.” The paper draws on quality-improvement guidance, management-system standards and research-design literature to connect the practical logic of identifying a performance gap with academic expectations for evidence, methodology, validity and inference.

At the center of the paper is what the authors call a five-element evidence chain, which functions as a decision rule for moving from an observed difference to a consequential practitioner research problem. The chain begins with an observed condition: what is happening in a relevant population, organization, sector or location. It then requires a normative condition, described as an objective and authoritative comparison point that may be temporal, spatial, comparative or standards-based. The third element is the issue itself, defined as a defensible difference between the observed and normative conditions. The fourth element, the associated effect, demands credible evidence connecting that issue to a meaningful negative or positive consequence. The final element, the practitioner research problem, is the issue considered together with its documented associated effect.

One of the framework’s most pointed rules concerns disparity. Under the authors’ decision logic, a difference between what is observed and what is expected is not automatically a consequential research problem. Researchers must also establish why the discrepancy matters by presenting credible evidence of an associated effect, and they are cautioned to avoid causal language unless the supporting research design actually warrants a causal conclusion. The framework thereby keeps association and causation distinct, reserving causal claims for studies whose designs can support them.

The authors also draw a sharp distinction between two constructs that beginning researchers may conflate: the practitioner research problem and the academic knowledge gap. In their account, the background of the problem and the problem statement establish the practical discrepancy and its associated effect, while the literature review establishes what remains unresolved in scholarship. The study purpose, research questions, methodology and design should then align with the knowledge the research is intended to produce. The framework is designed for beginning researchers and for experienced researchers who are new to practitioner research in business.

To keep the evidence itself trustworthy, the paper proposes an evidence-screening process built on five criteria: authority, currentness, methodological transparency, comparability and relevance. For the observed condition, the authors recommend relying on authoritative primary-source evidence released within the preceding five years. They acknowledge an important exception: applicable standards or historical benchmarks that anchor the normative condition may appropriately be older, since their value lies in their standing as reference points rather than in their recency.

McCalla framed the framework as a safeguard against premature closure in research design. “Practitioner researchers often bring substantial professional experience to the questions they study, and that experience is valuable, but it can also make it easy to simply begin with an explanation of what we think is wrong,” he said in the university’s announcement. “Using Define and Measure as procedural constraints encourages researchers to establish the condition and evidence first. That helps preserve an open research process rather than building the study around a preferred explanation or solution.”

Beyond the conceptual chain, the paper offers practical scaffolding for drafting. The authors provide a recommended sequence for developing the Background of the Problem, along with a model formulation and a hypothetical business example demonstrating how the evidence chain can be applied while preserving comparability between the observed and normative conditions. The worked example is explicitly hypothetical, and the authors limit the conclusions drawn in it to what the supporting evidence would justify, modeling the same restraint they prescribe for real dissertations. The complete white paper is available through the College of Doctoral Studies’ Research Hub.

The authors are explicit about the limits of their contribution. The white paper does not report results from an empirical study and does not present a validated research instrument. Its principal contribution is a proposed conceptual and drafting tool, and the authors state that its usefulness should be evaluated through application across different practitioner research settings and study designs. Rice is a fellow with the College’s Center for Educational and Instructional Technology Research, where his published work spans organizational change, knowledge management, work-life balance and digital consumer confidence, and he has participated in university research on doctoral students’ attitudes toward artificial intelligence chatbots and ChatGPT use in higher education. McCalla is a staff faculty member in the College of Doctoral Studies, a research fellow with the Center for Organizational Wellness, Engagement and Belonging, and chairs dissertations in the Doctor of Business Administration and Doctor of Management programs, drawing on more than 28 years of business leadership experience spanning organizational behavior and design, leadership theory, change management, human resource management and business development. Whether the framework measurably improves dissertation quality remains an open question that only future application and study can answer.

Subject of Research: A conceptual framework applying Lean Six Sigma DMAIC logic to doctoral research problem statement development

Article Title: Lean Six Sigma framework for developing doctoral research problem statements outlined in University of Phoenix white paper

Article References: Lean Six Sigma framework for developing doctoral research problem statements outlined in University of Phoenix white paper. Original publication (Bibliographic details unavailable.)

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Keywords: Lean Six Sigma, DMAIC, doctoral research, problem statement, dissertation, evidence chain, research methodology, practitioner research, knowledge gap, quality management, research design, University of Phoenix